anintroduction to measuring efficiency and productivity in agriculture by dea peter fandel slovak...
TRANSCRIPT
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An Introduction to MeasuringEfficiency and Productivity
in Agriculture by DEA
Peter FandelSlovak University of Agriculture
Nitra, Slovakia
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What are we going to cover?
Part 1 • Performance of a firm, productivity and efficiency
measurement• Introduction to DEA and DEA formulation• Input- and output orientation• Input- and output slacks• Returns to scale• Features of DEAPart 2• Software available
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Variety of forms in customary analyses:• Cost per unit• Profit per unit• Satisfaction per unit• usually in ratio form:
• This is a commonly used measure of efficiency, but also of productivity
Performance of a firm
Input
Output
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Production efficiency
Inputs Outputs
labour; production;
capital; sales;materials profit
Productivity• Partial productivity measures (output per worker
employed, output per worker hour, • Total factor productivity measures (all outputs, all inputs)
Transformation
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Productivity and EfficiencySingle input and single output case
Farm A B C D E F G H
Employees 2 3 3 4 5 5 6 8
Sale 1 3 2 3 4 2 3 5
Sale/Empl.(productivity)
0.5 1 0.667 0.75 0.8 0.4 0.5 0.625
Efficiency –prod. relative to B
0.5 1 0.667 0.75 0.8 0.4 0.5 0.625
Sales per employee of others
Sales per employee of B0 ≤ ≤ 1
Relative efficiency: productivity / max. productivity
Source: Cooper, W.W. – Seiford, L.M., - Tone, K., 2002
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Productivity and EfficiencySingle input and single output case
0
1
2
3
4
5
6
0 2 4 6 8 10
Employee
Sal
es
A
B
C
D
E
F
G
HEfficiency frontier
Source: Cooper, W.W. – Seiford, L.M., - Tone, K., 2002
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Productivity and EfficiencySingle input and single output case
0
0,5
1
1,5
2
2,5
3
3,5
0 0,5 1 1,5 2 2,5 3 3,5
A1
A
B
A2
Improvement of input
Improvement of output
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Productivity and EfficiencyTwo inputs and one output case
A B C D E FEmployee x1 4 7 8 4 2 10Land x2 3 3 1 2 4 1Sale y 1 1 1 1 1 1
4; 3 7; 3
8; 1
4; 2
2; 4
10; 1
0
0,5
1
1,5
2
2,5
3
3,5
4
4,5
0 2 4 6 8 10 12
Employee / Sales
Lan
d /
Sal
es
A
E
F
D
C
B
Efficiency frontier
Production possibility set
P(3,4;2,6)Efficiency of A =
0P / 0A = 0.8571
{ {
Source: Cooper, W.W. – Seiford, L.M., - Tone, K., 2002
28.46.24.3 22
d(0,D) =
d(0,A) =
534 22
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Productivity and EfficiencyOne input and two outputs case
534 22
3
204)
3
16( 22
A B C D E F GEmployee x 1 1 1 1 1 1 1Contracts y1 1 2 3 4 4 5 6Sales y2 5 7 4 3 6 5 2
Efficiency of D =
4; 6
5; 5
6; 2
4; 3
3; 4
1; 5
2; 7
0
1
2
3
4
5
6
7
8
0 1 2 3 4 5 6 7
Contracts / Employee
Sa
les
/ E
mp
loy
ee A
C
D
G
F
E
BQ
P(16/3;4)
(1,4;7)
d(0,D)
d(0,P)= 0.75
d(0,D) =
d(0,P) =
534 22
Production possibility set
Source: Cooper, W.W. – Seiford, L.M., - Tone, K., 2002
d(0,P)
d(0,D)= 1.33
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Efficiency measurementwhen more inputs and more outputs
Efficiency =Output(1) + Output(2) + … + Output(s)
Input(1) + Input(2) + … + Input(m)
BUT• Firm outputs cannot be added together directly• Firm inputs cannot be added together directly
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Efficiency measurementwhen more inputs and more outputs
Efficiency = Output(1)*Weight(1) + Output(2)*Weight(2) + … + Output(s) )*Weight(s)
Input(1)*Weight(1) + Input(2)*Weight(2) + … + Input(m)*Weight(m)
BUT • It is necessary to estimate weights• When weights are known, it is easy to calculate efficiency measures
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Efficiency measurementwhen more inputs and more outputs
A B C D E FOutput1 U1= 1 100 150 160 180 94 230Output2 U2= 3 90 50 55 72 66 90Input1 V1= 5 20 19 25 27 22 55Input2 V2= 1 151 131 160 168 158 255
370 300 325 396 292 500251 226 285 303 268 530
1,47 1,33 1,14 1,31 1,09 0,941,00 0,90 0,77 0,89 0,74 0,64
Productivity (TFP)Efficiency score
FirmsWeights
Weighted outputsWeighted inputs
When fixed weights are available:
Source: Cooper, W.W. – Seiford, L.M., - Tone, K., 2002
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Efficiency measurementwhen more inputs and more outputs
When fixed weights are not available:
• Linear programming (LP) is used to calculate both efficiency measure and the weights for each firm by comparison with other firms
• The specific application of LP is called Data Envelopment Analysis - DEA
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What are we going to cover?
Part 1 • Performance of a firm, productivity and efficiency
measurement• Introduction to DEA and DEA formulation• Input- and output orientation• Input- and output slacks• Returns to scale• Features of DEAPart 2• Software available
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Introduction to DEA and DEA formulation
0
1
2
3
4
5
6
0 2 4 6 8 10
Employee
Sal
es
A
B
C
D
E
F
G
HEfficiency frontier
Regression line
y=0.662x
Source: Cooper, W.W. – Seiford, L.M., - Tone, K., 2002
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Introduction to DEA and DEA formulation
• Technical efficiency (TE)
Maximisation of outputs for given set of inputs• Allocative efficiency (AE)
use of inputs in optimal proportions given their respective prices and production technology
• Economic efficiency
Combination of TE and AE• DMU – Decision Making Unit
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DEA formulation
m
iii
s
rrr
xv
yu
10
10
max : TEi = 0 ≤ ≤ 1
yr = quantity of output r;vr = weight attached to output r;yi = quantity of input i;vi = weight attached to input ifor s outputs and m inputs
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DEA formulation Fractional programming problem
max θ =
≤ 1 (j = 1, …, n)
u1y10+u2y20+ … + usys0
v1x10+v2x20+ … + vmxm0
u1y1j+u2y2j+ … + usysj
v1x1j+v2x2j+ … + vmxmj
v1,v2, … , vm ≥ 0
u1,u2, … , um ≥ 0
subject to
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DEA formulation (primal)Transformation to a linear programming problem
max θ =
(j = 1, …, n)
u1y10+u2y20+ … + usys0
subject to v1x10+v2x20+ … + vmxm0 = 1
u1y1j+u2y2j+ … + usysj ≤ v1x1j+v2x2j+ … + vmxmj
v1,v2, … , vm ≥ 0
u1,u2, … , um ≥ 0
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DEA formulation (primal)An example of linear programming problem for firm C
max θ = 160u1+55u2
subject to 25v1+160v2 = 1
A: 100u1+90u2 ≤
19v1+131v2
C: 160u1+55u2 ≤
20v1+151v2
B: 150u1+50u2 ≤
27v1+168v2
E: 94u1+66u2 ≤
25v1+160v2
D: 180u1+72u2 ≤
22v1+158v2
A B C D E FOutput1 100 150 160 180 94 230Output2 90 50 55 72 66 90Input1 20 19 25 27 22 55Input2 151 131 160 168 158 255v2
u1u2v1
FirmsWeights
F: 230u1+90u2 ≤ 55v1+255v2
v1,v2 ≥ 0 u1,u2 ≥ 0
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Primal DEA results1. A firm (DMU0) is efficient if θ* = 1 and there exists at
least one optimal solution (u*, v*), with u* > 0 and v* > 02. Otherwise a firm (DMU0) is inefficient3. If a firm (DMU0) is inefficient, at least one constraint of
the LP problem must be satisfied as an equation. Firms for which constraints are of this character are called reference set, peer group, or benchmark.
4. Optimal θ* is the technical efficiency measure. It says to what extend inputs of DMU0 should be equiproportionally reduced, or what level of possible outputs is DMU0
generating from its inputs.
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Primal optimum for the firm CLinear programFirm C u1 u2 v1 v2
0,003583 0,005625 0 0,00625 ThetaMax THETA 160 55 0,882708
LHS Rel RHSNormalization 25 160 1 = 1Firm A 100 90 -20 -151 -0,07917 <= 0Firm B 150 50 -19 -131 4,54E-14 <= 0Firm C 160 55 -25 -160 -0,11729 <= 0Firm D 180 72 -27 -168 -9,8E-13 <= 0Firm E 94 66 -22 -158 -0,27942 <= 0Firm F 230 90 -55 -255 -0,26333 <= 0
Weights
Peers for the firm C: firm B and firm D
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Primal optimum for all firms
u1 u2 v1 v21. Firm A 0,003407 0,007326 0,005917 0,005839 1 1,2,42. Firm B 0,003883 0,00835 0,006744 0,006655 1 1,2,43. Firm C 0,003583 0,005625 0 0,00625 0,882708 2,44. Firm D 0,002987 0,006422 0,005187 0,005119 1 1,2,45. Firm E 0,003407 0,007326 0,005917 0,005839 0,763499 1,2,46. Firm F 0,002248 0,003529 0 0,003922 0,834771 2,4
WeightsTHETA Peers
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DEA formulation (dual)
Disadvantages of primal DEA:• Usually more optimal solutions• Too many constraints (the number is
equivalent to the number of firms evaluated)
• Complicated way of efficient DMU identification
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DEA formulation (dual)Primal DEA problem for the firm C (adapted)
max θ = 160u1+55u2
θ : 25v1+160v2 = 1
λ1: 100u1+90u2
-19v1-131v2 ≤ 0
λ3 : 160u1+55u2
-20v1-151v2 ≤ 0
λ2 : 150u1+50u2
-27v1-168v2 ≤ 0
λ5 : 94u1+66u2
-25v1-160v2 ≤ 0
λ4 : 180u1+72u2
-22v1-158v2 ≤ 0
λ6 : 230u1+90u2 -55v1-255v2 ≤ 0
v1,v2 ≥ 0 u1,u2 ≥ 0
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DEA formulation (dual)Dual DEA problem for the firm C
min θsubject to
100 λ1+150 λ2+160 λ3+180 λ4+94 λ5+230 λ6 ≥ 160
90 λ1+ 50 λ2+ 55 λ3+ 72 λ4+66 λ5+ 90 λ6 ≥ 55
25θ - 25 λ1 - 19 λ2 - 25λ3 - 27 λ4 - 22 λ5 - 55λ6 ≥ 0
160θ - 151 λ1 - 31λ2 - 160 λ3 -168 λ4 -158 λ5 - 255 λ6 ≥ 0
λ1, λ2, λ3, λ4, λ5, λ6 ≥ 0
θ - free
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DEA formulation (dual)general formulation
min θ
subject to
yr1 λ1+ yr2 λ2 + … + yrn λn ≥ yr0 , r = 1, 2,…, s
- θxi0 + xi1 λ1 + xi2 λ2 + … + ximλn ≤ 0 , i = 1,2, …, m
λ1, λ2, …, λn ≥ 0
θ - free
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Dual DEA results1. A firm (DMU0) is efficient if θ* = 1, all λj =0, except λ0=12. A firm (DMU0) is inefficient if θ* < 1. 3. If a firm (DMU0) is inefficient, nonzero λj point at peers.
A convex combination of peer inputs and outputs with λj gives a virtual DMU at the frontier
4. Optimal θ* in this case gives so called Farrell input oriented efficiency measure
5. Constant returns to scale are assumed6. Output oriented measure φ = 1/ θ
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Dual DEA resultsall firms
θ λ1 λ2 λ3 λ4 λ5 λ6Firm A 1,0000 1,0000 0,0000 0,0000 0,0000 0,0000 0,0000Firm B 1,0000 0,0000 1,0000 0,0000 0,0000 0,0000 0,0000Firm C 0,8827 0,0000 0,9000 0,0000 0,1389 0,0000 0,0000Firm D 1,0000 0,0000 0,0000 0,0000 1,0000 0,0000 0,0000Firm E 0,7635 0,5794 0,0572 0,0000 0,1526 0,0000 0,0000Firm F 0,8348 0,0000 0,2000 0,0000 1,1111 0,0000 0,0000
Firms A, B, D are efficientFirms C, E, F are inefficientTarget values of inputs and outputs
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What are we going to cover?
Part 1 • Performance of a firm, productivity and efficiency
measurement• Introduction to DEA and DEA formulation• Input- and output orientation• Input- and output slacks• Returns to scale• Features of DEAPart 2• Software available
![Page 31: AnIntroduction to Measuring Efficiency and Productivity in Agriculture by DEA Peter Fandel Slovak University of Agriculture Nitra, Slovakia](https://reader036.vdocument.in/reader036/viewer/2022070412/5697bf8d1a28abf838c8c90b/html5/thumbnails/31.jpg)
Input and output efficiency
Input oriented measures keep output fixed• input oriented technical efficiency (TEi) by how much
can input quantities be proportionally reduced holdingoutput constant
Output oriented measures keep input fixed• output oriented technical efficiency (TEo) by how
much can output quantities be proportionally expandedholding input constant
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Input and output efficiency
0
0,5
1
1,5
2
2,5
3
3,5
0 0,5 1 1,5 2 2,5 3 3,5
A1
A
B
A2
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Input and output orientated DEA
Input oriented DEAmin θ s.t.
Yλ ≥ y0
- θx0 +Xλ ≤ 0 λ ≥ 0
0 ≤ θ ≤ 1
Output oriented DEAmax φ s.t.
- φy0 + Yλ ≥ 0
Xλ ≤ x0
λ ≥ 0
1 ≤ φ ≤ +∞
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• Output- and input-oriented models will estimate exactly the same frontier
• The same set of DMUs will be identified as efficient
• Efficiency measures of inefficient DMUs may differ
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What are we going to cover?
Part 1 • Performance of a firm, productivity and efficiency
measurement• Introduction to DEA and DEA formulation• Input- and output orientation• Input- and output slacks• Returns to scale• Features of DEAPart 2• Software available
![Page 36: AnIntroduction to Measuring Efficiency and Productivity in Agriculture by DEA Peter Fandel Slovak University of Agriculture Nitra, Slovakia](https://reader036.vdocument.in/reader036/viewer/2022070412/5697bf8d1a28abf838c8c90b/html5/thumbnails/36.jpg)
Input slacks
4; 3 7; 3
6; 1
4; 2
2; 4
10; 1
0
0,5
1
1,5
2
2,5
3
3,5
4
4,5
0 2 4 6 8 10 12
Employee / Sales
Lan
d /
Sal
es
A
E
F
D
C
B
Q
P(3,4;2,6)
Farrell efficiency vs Pareto-Koopmans efficiencyAdapted from: Cooper, W.W. – Seiford, L.M., - Tone, K., 2002
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Output slacks
4; 6
5; 5
6; 2
4; 3
3; 4
1; 5
2; 7
0
1
2
3
4
5
6
7
8
0 1 2 3 4 5 6 7
Contracts / Employee
Sa
les
/ E
mp
loy
ee A
C
D
G
F
E
BQ
P(16/3;4)
(1,4;7)
Farrell efficiency vs. Pareto-Koopmans efficiencySource: Cooper, W.W. – Seiford, L.M., - Tone, K., 2002
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Treatment of slacks
Input orientated DEAmin θ– ε ∙1's+ - ε ∙1's
- s.t.
Yλ - s+ = y0
- θx0 +Xλ + s - = 0 λ ≥ 0
0 ≤ θ ≤ 1
Output orientated DEA
max φ
s.t.
- φy0 + Yλ - s+ = 0
Xλ + s - = x0
λ ≥ 0
1 ≤ φ < +∞
DMU is efficient if and only if θ = 1 and all slacks s+ = 0, s - = 0
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What are we going to cover?
Part 1 • Performance of a firm, productivity and efficiency
measurement• Introduction to DEA and DEA formulation• Input- and output orientation• Input- and output slacks• Returns to scale• Features of DEAPart 2• Software available
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y
PC
x0
QR
PAPV
CRS Frontier
NiRS Frontier
VRS Frontier
B
TECRS = APC/AP, TEVRS = APV/AP, ER = APC/APV
Returns to scale
Source: Cooper, W.W. – Seiford, L.M., - Tone, K., 2002
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What are we going to cover?
Part 1 • Performance of a firm, productivity and efficiency
measurement• Introduction to DEA and DEA formulation• Input- and output orientation• Input- and output slacks• Returns to scale• Features of DEAPart 2• Software available
![Page 42: AnIntroduction to Measuring Efficiency and Productivity in Agriculture by DEA Peter Fandel Slovak University of Agriculture Nitra, Slovakia](https://reader036.vdocument.in/reader036/viewer/2022070412/5697bf8d1a28abf838c8c90b/html5/thumbnails/42.jpg)
Features of DEA• We use LP to solve DEA formulations• It assigns weights to each DMU to put them in• the best possible light• DEA constructs a piecewise linear frontier which
envelops the other inefficient DMUs (intersecting planes in 3D-space
• DEA measures inefficiency as the radial distance from the inefficient unit to the frontier
• The inefficiency score is unit invariant• DEA is a data driven approach
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Features of DEA
Advantages:• Easy to use• Allows multiple inputs and multiple outputs• Does not require specification of functional form• Does not require a prior specification of weightsDisadvantages:• No account of error / random noise• Non-parametric method – no goodness of fit
measures, model specification measures
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Software• DEAP version 2.1 by Tim Coelli
– Centre for Efficiency and Productivity Analysis (CEPA)– Coelli, T.J. (1996), “A Guide to DEAP Version 2.1: A Data Envelopment Analysis (Computer) Program”, CEPA Working Paper 96/8, Department of Econometrics, University of New England, Armidale NSW Australia.– Freely available at http://www.uq.edu.au/economics/cepa/software.htm
• EMS: Efficiency Measurement System version 1.3 – University of Dortmund, by Holger Scheel– Available freely at http://www.wiso.uni-dortmund.de/lsfg/or/scheel/ems/– Uses Excel or ASCII data files
• DEAFrontier – by Joe Zhu– Zhu, J. (2003) Quantitative Models for Performance Evaluation and Benchmarking Data Envelopment Analysis with Spreadsheets and DEA Excel Solver, Kluwer Academic Publishers: Boston.– Excel Solver– Details at http://www.deafrontier.com/software.html
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References
1. Cooper, W.W. – Seiford, L.M., - Tone, K. 2002. Data Envelopment Analysis. A Comprehensive Text with Models, Applications, References and DEA-Solver Software
2. Jacobs Rovena, 2005. An Introduction to Measuring Efficiency and Productivity in Public Sector, Workshop material, Data Envelopment Analysis workshop, Centre for Health Economics, University of York, 10-11 January 2005